Abstract
Text categorization is the problem of automatically assigning predefined categories to natural language texts. A major difficulty of this problem stems from the high dimensionality of its feature space. Reducing the dimensionality, or selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to this area. In this paper, we propose a neuro-genetic approach to feature selection in text categorization. Candidate feature subsets are evaluated by using three-layer feedforward neural networks. The Baldwin effect concerns the tradeoffs between learning and evolution. It is used in our research to guide and improve the GA-based evolution of the feature subsets. Experimental results show that our neuro-genetic algorithm is able to perform as well as, if not better than, the best results of neural networks to date, while using fewer input features.
Original language | English (US) |
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Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
Publisher | IEEE Computer Society |
Pages | 2924-2927 |
Number of pages | 4 |
Volume | 4 |
State | Published - 1999 |
Event | International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA Duration: Jul 10 1999 → Jul 16 1999 |
Other
Other | International Joint Conference on Neural Networks (IJCNN'99) |
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City | Washington, DC, USA |
Period | 7/10/99 → 7/16/99 |
ASJC Scopus subject areas
- Software